Machine Learning Based Model to Identify Firewall Decisions to Improve Cyber-Defense

Qasem Abu Al-Haija, Abdelraouf Ishtaiwi

Abstract


A firewall system is a security system to ensure traffic control for incoming and outgoing packets passing through communication networks by applying specific decisions to improve cyber-defense and decide against malicious packets. The filtration process matches the traffic packets against predefined rules to preclude cyber threats from getting into the network. Accordingly, the firewall system proceeds with either to “allow,” “deny,” or “drop/reset” the incoming packet. This paper proposes an intelligent classification model that can be employed in the firewall systems to produce proper action for every communicated packet by analyzing packet attributes using two machine learning methods, namely, shallow neural network (SNN), and optimizable decision tree (ODT). Specifically, the proposed models have used to train and classify the Internet Firewall-2019 dataset into three classes: “allow, “deny,” and “drop/reset.” The experimental results exhibited our classification model's superiority, scoring an overall accuracy of 99.8%, and 98.5% for ODT, and SNN respectively. Besides, the suggested system was evaluated using many evaluation metrics, including confusion matrix parameters (TP, TN, FP, FN), true positive rate (TPR), false-negative rate (FNR), positive predictive value (PPV), false discovery rate (FDR), and the receiver operating characteristic (ROC) curves for the developed three-class classifier. Ultimately, the proposed system outpaced many existing up-to-date firewall classification systems in the same area of study.


Keywords


Artificial intelligence; shallow neural network; decision tree; network security; firewalls; firewall logs; classification.

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References


W. Noonan, I. Dubrawsky, “Firewall fundamentals”, Pearson Education, 2006.

Q. A. Al-Haija, S. Zein-Sabatto, "An Efficient Deep-Learning-Based Detection and Classification System for Cyber-Attacks in IoT Communication Networks" Electronics, MDPI, vol. 9, no. 12: paper no. 2152., 2020.

E. Ucar, E. Ozhan, “The Analysis of Firewall Policy Through Machine Learning and Data Mining”, Wireless Personal Communication, Springer, vol. 96, p.p. 2891–2909, 2017.

G. Caspi, “Introducing Deep Learning: Boosting Cybersecurity with an Artificial Brain. Informa Tech” Dark Reading, Analytics http://www.darkreading.com/analytics, 2016.

Q.A. Al-Haija, C.D. McCurry, S. Zein-Sabatto, “Intelligent Self-reliant Cyber-Attacks Detection and Classification System for IoT Communication Using Deep Convolutional Neural Network”, Selected Papers from 12th International Networking Conference. INC 2020. Lecture Notes in Networks and Systems, vol.180. Springer, 2021.

J. Brownlee, “4 Types of Classification Tasks in Machine Learning”, Python Machine Learning, Machine Learning Mastery, 2020.

S. Haykin, “Neural Networks and Learning Machines. 3rd Edition, Pearson publications, ISBN-13: 978-0-13-147139-9, 2009.

C. C. Aggarwal, “Machine Learning with Shallow Neural Networks”, Neural Networks and Deep Learning. Springer, 2019.

Fei-Fei. CS231n: Convolutional Neural Networks for Visual Recognition. Computer Science, Stanford University. Available online: http://cs231n.stanford.edu, 2019.

J. S. Meneses, Z.R. Chavez, J.G. Rodriguez, "Compressed kNN: K-Nearest Neighbors with Data Compression" Entropy, MDPI, vol. 21, no. 3, paper no. 234, 2019.

Y.Y. Song, Y. Lu, “Decision tree methods: applications for classification and prediction. Shanghai Arch Psychiatry”, PMID: 26120265; PMCID: PMC4466856, vol. 27, no.2, p.p.130-5, 2015.

B. A. Tama, K. H. Rhee, “An extensive empirical evaluation of classifier ensembles for intrusion detection task”, International Journal Computer Systems Science and Engineering, CRL Publishing Ltd, vol. 32, no.2, p.p.149-158, 2017.

A. Ghose, “Support Vector Machine (SVM) Tutorial: Learning SVMs from examples”. Medium: towards data science, 2017.

R. Garg, “Types of Classification Algorithms”, Analytics India Magazine, 2018.

F. Ertam, M. Kaya, "Classification of firewall log files with multi-class support vector machine," in Proc. Of 6thInternational Symposium on Digital Forensic and Security (ISDFS), Antalya, pp. 1-4, 2019.

Q. A. Al-Haija, L. Tawalbeh, "Autoregressive Modeling and Prediction of Annual Worldwide Cybercrimes for Cloud Environments," in Proc. Of 10th International Conference on Information and Communication Systems (ICICS), 2019, pp. 47-51.

D. Appelt, C. D. Nguyen, A. Panichella, L. C. Briand, "A Machine-Learning-Driven Evolutionary Approach for Testing Web Application Firewalls," IEEE Transactions on Reliability, vol. 67, no. 3, pp. 733-757, 2018, doi: 10.1109/TR.2018.2805763.

E. Ucar, E. Ozhan, “The Analysis of Firewall Policy Through Machine Learning and Data Mining”, Wireless Personal Communication, Springer, vol. 96, p.p. 2891–2909, 2017.

A. M. Vartouni, M. Teshnehlab, S. S. Kashi, “Leveraging deep neural networks for anomaly-based web application firewall”, IET Information Security, vol. 13, p.p. 352-361, 2019.

F. Ertam, “An efficient hybrid deep learning approach for internet security”, Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535, 2019

J.J. Praise, R.J Raj, J.V. Benifa, “Development of Reinforcement Learning and Pattern Matching (RLPM) Based Firewall for Secured Cloud Infrastructure”, Wireless Personal Communication, Springer, vol.115, p.p. 993–1018, 2020.

G. Bendiab, S. Shiaeles, A. Alruban, N. Kolokotronis, “IoT Malware Network Traffic Classification using Visual Representation and Deep Learning”, in Proc. Of 6th IEEE Conference on Network Softwarization (NetSoft), Ghent, Belgium, 29 June–3 July 2020; pp. 444–449.

R. Shire, S. Shiaeles, K. Bendiab, B. Ghita, N. Kolokotronis, “Malware Squid: A Novel IoT Malware Traffic Analysis Framework Using Convolutional Neural Network and Binary Visualization”, in Proc. Of Internet of Things, Smart Spaces, and Next Generation Networks and Systems. Lecture Notes in Computer Science; Springer, vol.11660, 2019

I. Baptista, S. Shiaeles, N. Kolokotronis, “A Novel Malware Detection System Based On Machine Learning and Binary Visualization”, in Proc. Of IEEE International Conference on Communications (IEEE ICC), China, pp. 1–6, 2019.

K.A. Taher, B.M. Jisan, M.M Rahman, “Network Intrusion Detection using Supervised Machine Learning Technique with Feature Selection”, in Proc. Of International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST), South Asia, 10–12 January 2019; pp. 643–646.

X. Gao, C. Shan, C. Hu, Z. Niu, Z. Liu, “An Adaptive Ensemble Machine Learning Model for Intrusion Detection”, IEEE Access, vol. 7, p.p. 82512–82521, 2019.

Q. A. Al-Haija, M. Alkhatib, A. B. Jaafar, “Choices on Designing Gf (P) Elliptic Curve Coprocessor Benefiting from Mapping Homogeneous Curves in Parallel Multiplications”, International Journal on Computer Science and Engineering (IJCSE), ISSN: 0975-3397, vol. 3 no. 2, 2011.

S. Sapre, P. Ahmadi, K. Islam, “A Robust Comparison of the KDDCup99 and NSL-KDD IoT Network Intrusion Detection Datasets through Various Machine Learning Algorithms”, arXiv:1912.13204v1, 2019.

M.M. Chowdhury, F. Hammond, G. Konowicz, C. Xin, H. Wu, J. Li, “A few-shot deep learning approach for improved intrusion detection”, 2017 in Proc. Of IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON), NY, USA, 19–21 October 2017; pp. 456–462.

A. Javaid, Q. Niyaz, W. Sun, M. Alam, “A Deep Learning Approach for Network Intrusion Detection System”, in Proc. Of 9th EAI International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS), NY, USA, 24 May 2016; pp. 21–26.

Y. Imamverdiyev, L. Sukhostat, “Anomaly detection in network traffic using extreme learning machine”, in Proc. Of IEEE 10th International Conference on Application of Information and Communication Technologies (AICT), Azerbaijan, 12–14 October 2016; pp. 1–4.

UCI: Machine Learning Repository, “Internet Firewall Data Set”, Center for Machine Learning and Intelligent Systems, 2019.

A. Wang, “Encode Smarter: How to Easily Integrate Categorical Encoding into Your Machine Learning Pipeline”, Feature Labs. https://blog.featurelabs.com, 2019.

Q. A. Al-Haija, M. Smadi, S. Zein-Sabatto, “Multi-Class Weather Classification Using ResNet-18 CNN for Autonomous IoT and CPS Applications” in Proc. Of IEEE 7th Annual Conference on Computational Science & Computational Intelligence (CSCI'20), Las Vegas, USA, 2020.

K.E. Koech, “Cross-Entropy Loss Function”, Medium: towards data science, 2020.

A. I. Pîrîu, M. Leonte, N. Postolachi and D. T. Gavrilut, "Optimizing Cleanset Growth by Using Multi-Class Neural Networks," in Proc. Of 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), Romania, pp. 425-429, 2018.

A. Banjongkan, et. al., “A Comparative Study of Learning Techniques with Convolutional Neural Network Based on HPC-Workload Dataset” Inter. Journal of Machine Learning and Computing, vol. 10, no.1, 2020.

S. Allagi and R. Rachh, "Analysis of Network log data using Machine Learning," in Proc. Of IEEE 5th International Conference for Convergence in Technology, India, pp. 1-3, 2019.




DOI: http://dx.doi.org/10.18517/ijaseit.11.4.14608

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